Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 11,817Sort Recent · Most cited
  1. 2022
    Dynamics-Adaptive Continual Reinforcement Learning via Progressive ContextualizationTiantian Zhang, Zichuan Lin, Yuxing Wang … Xiu LiTNNLS · Tencent (China) · Tsinghua University · +1
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  2. 2021
    Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge DistillationTiantian Zhang, Xueqian Wang, Bin Liang, Bo YuanTNNLS · University Town of Shenzhen · Tsinghua University
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  3. 2020
    Triple-Memory Networks: A Brain-Inspired Method for Continual LearningLiyuan Wang, Bo Lei, Qian Li … Yi ZhongTNNLS · Chinese Institute for Brain Research · Center for Life Sciences · +1
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  4. 2021
    Memory Recall: A Simple Neural Network Training Framework Against Catastrophic ForgettingBaosheng Zhang, Yuchen Guo, Yipeng Li … Qionghai DaiTNNLS · Tsinghua University · Tsinghua–Berkeley Shenzhen Institute
  5. 2020
    IncDet: In Defense of Elastic Weight Consolidation for Incremental Object DetectionLiyang Liu, Zhanghui Kuang, Yimin Chen … Wayne ZhangTNNLS · University Town of Shenzhen · Tsinghua University · +2
  6. 2020
    Lifelong Visual-Tactile Cross-Modal Learning for Robotic Material PerceptionWendong Zheng, Huaping Liu, Fuchun SunTNNLS · Hebei University of Technology · Tsinghua University
  7. 2020
    Generative Memory for Lifelong LearningXin Su, Shangqi Guo, Tian Tan, Feng ChenTNNLS · Beijing Advanced Sciences and Innovation Center · Tsinghua University · +1
  8. 2018
    $L1$ -Norm Batch Normalization for Efficient Training of Deep Neural NetworksShuang Wu, Guoqi Li, Lei Deng … Luping ShiTNNLS · Tsinghua University · University of California, Santa Barbara · +1
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.